Principal component regression analysis demonstrates the collinearity-free effect of sub estimated climatic variables on tree growth in the central Amazon

Abstract

Introduction: Climatic variables show a seasonal pattern in the central Amazon, but the intra-annual variability effect on tree growth is still unclear. For variables such as relative humidity (RH) and air vapor pressure deficit (VPD), whose individual effects on tree growth can be underestimated, we hypothesize that such influences can be detected by removing the effect of collinearity between regressors. Objective: This study aimed to determine the collinearity-free effect of climatic variability on tree growth in the central Amazon. Methods: Monthly radial growth was measured in 325 trees from January 2013 to December 2017. Irradiance, air temperature, rainfall, RH, and VPD data were also recorded. Principal Component Regression was used to assess the effect of micrometeorological variability on tree growth over time. For comparison, standard Multiple Linear Regression (MLR) was also used for data analysis. Results: Tree growth increased with increasing rainfall and relative humidity, but it decreased with rising maximum VPD, irradiance, and maximum temperature. Therefore, trees grew more slowly during the dry season, when irradiance, temperature and VPD were higher. Micrometeorological variability did not affect tree growth when MLR was applied. These findings indicate that ignoring the correlation between climatic variables can lead to imprecise results. Conclusions: A novelty of this study is to demonstrate the orthogonal effect of maximum VPD and minimum relative humidity on tree growth.